The accurate reconstruction of three-dimensional shapes and scenes from imagery is an important and quickly advancing field of computer vision. In particular, stereo reconstruction and other associated algorithms can be used as part of a processing pipeline for automatically reconstructing a three-dimensional model of a scene from a set of two-dimensional input images that depict the scene. Such reconstruction functionality has broad application, including, but not limited to, three-dimensional mapping and navigation, augmented and virtual reality, three-dimensional content modeling for games and/or films, and other applications.
However, one problem experienced by certain existing stereo reconstruction pipelines is that errors introduced during early stages of the pipeline are carried on and amplified by subsequent stages of the pipeline. Cascading and amplification of initial errors can cause the final reconstruction results to be significantly deteriorated.
Therefore, processing pipelines that include stages for minimizing or otherwise identifying and eliminating errors or other inaccuracies are needed.